The present invention is related to a communication system with digital receiver channel equalization.
In a digital receiver, dynamic equalization is usually needed to compensate for time-variant channel distortion and eliminate inter-symbol-interference (ISI). FIG. 1 gives the typical DSP (digital signal processing) elements and procedure. Signal x is input to receiver system 100 and is first fed to pre-processing block 102 which, for example, can be perform sampling rate conversion and fixed channel-effect compensation/pre-equalization, among others. Signal y from block 102's output is coupled to dynamic channel equalization 104 which usually uses FIR (Finite Impulse Response) filter. In combination, blocks 102 and 104 form block 110 that performs digital receiver channel equalization.
Block 104's input and output signals (y and z) are also connected to channel estimation block 106 to track for channel changes and update channel parameters for block 104. Such parameters can be FIR filter coefficients, in case FIR filter is applied in channel equalization block 104. Channel equalization block 104 then takes the updated parameters to equalize its next input signal. Signal output from 104 is passed to post-processing block 108.
However, in some cases due to combined processing, for example pre-processing 102 and channel equalization 104 are merged to processing block 110, such input signal to channel estimation block 106 will not be available. One example is when dynamic equalization combined with pre-processing in frequency-domain where FFT (Fast Fourier Transform) is applied to the input signal, while channel estimation is still performed in time domain. In such case there must be alternative ways to re-generate the expected input signal.
Conventional systems may re-generate the expected input by applying de-convolution to the output signal z. Convolution is the DSP operation for two functions f(n) and g(n), producing a third function defined as:
            (              f        *        g            )        ⁡          [      n      ]        ⁢      =    def    ⁢            ∑              m        =                  -          ∞                    ∞        ⁢                  ⁢                  f        ⁡                  [          m          ]                    ⁢              g        ⁡                  [                      n            -            m                    ]                    
For example, f can be input signal, and g can be a FIR filter function. De-convolution can be done using a reverse of estimated channel parameter (i.e., reverse of FIR filter) to do convolution with signal output, which is:(f(n)*g(n))*g−1(n)=f(n)
However, it can be difficult to get the reverse of channel parameters for the above solution. The added complexity in calculating de-convolution may require more powerful hardware which is power hunger. Further, additional errors may be caused when simplifying assumptions or engineering approximations are used.